/*
 * This file is part of the UEA Time Series Machine Learning (TSML) toolbox.
 *
 * The UEA TSML toolbox is free software: you can redistribute it and/or 
 * modify it under the terms of the GNU General Public License as published 
 * by the Free Software Foundation, either version 3 of the License, or 
 * (at your option) any later version.
 *
 * The UEA TSML toolbox is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License along
 * with the UEA TSML toolbox. If not, see <https://www.gnu.org/licenses/>.
 */
 
package machine_learning.classifiers.ensembles.weightings;

import machine_learning.classifiers.ensembles.AbstractEnsemble.EnsembleModule;

/**
 * Simply uses the modules train acc as it's weighting. Extension: can raise the accuracy
 * to some power in order to scale the relative difference in accuracy between modules 
 * 
 * e.g, if raising all accuracies to power of 2, weights (0.7, 0.8, 0.9) become (0.49, 0.64, 0.81)
 * 
 * @author James Large
 */
public class TrainAcc extends ModuleWeightingScheme {

    private double power = 1.0;
    
    public TrainAcc() {
        uniformWeighting = true;
        needTrainPreds = true;
    }
    
    public TrainAcc(double power) {
        this.power = power;
        uniformWeighting = true;
        needTrainPreds = true;
    }
    
    public double getPower() { 
        return power;
    }
    
    public void setPower(double power) {
        this.power = power;
    }
    
    @Override
    public double[] defineWeighting(EnsembleModule module, int numClasses) {
        return makeUniformWeighting(Math.pow(module.trainResults.getAcc(), power), numClasses);
    }
    
    @Override
    public String toString() {
        return this.getClass().getSimpleName() + (power==1.0 ? "" : "(" + power + ")");
    }
    
}
